While we will present our algorithm as a randomized algorithm, it can
be derandomized using the standard method of conditional
expectation. See Section  for details. In
order to achieve the result, we introduce the following two techniques
for Correlation Clustering. Our result also implies a marginally better constant factor
approximation for the problem of fitting a tree metric or an
ultrametric through the framework
of [1]}, [2]}.
